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Czasopismo
2018 | Vol. 66, no. 1 | 71--80
Tytuł artykułu

Improving Waveform Inversion using Modified Interferometric Imaging Condition

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Similar to the reverse-time migration, full waveform inversion in the time domain is a memory-intensive processing method. The computational storage size for waveform inversion mainly depends on the model size and time recording length. In general, 3D and 4D data volumes need to be saved for 2D and 3D waveform inversion gradient calculations, respectively. Even the boundary region wavefield-saving strategy creates a huge storage demand. Using the last two slices of the wavefield to reconstruct wavefields at other moments through the random boundary, avoids the need to store a large number of wavefields; however, traditional random boundary method is less effective at low frequencies. In this study, we follow a new random boundary designed to regenerate random velocity anomalies in the boundary region for each shot of each iteration. The results obtained using the random boundary condition in less illuminated areas are more seriously affected by random scattering than other areas due to the lack of coverage. In this paper, we have replaced direct correlation for computing the waveform inversion gradient by modified interferometric imaging, which enhances the continuity of the imaging path and reduces noise interference. The new imaging condition is a weighted average of extended imaging gathers can be directly used in the gradient computation. In this process, we have not changed the objective function, and the role of the imaging condition is similar to regularization. The window size for the modified interferometric imaging condition-based waveform inversion plays an important role in this process. The numerical examples show that the proposed method significantly enhances waveform inversion performance.
Wydawca

Czasopismo
Rocznik
Strony
71--80
Opis fizyczny
Bibliogr. 47 poz.
Twórcy
autor
  • Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China, guoxuebao@mail.iggcas.ac.cn
  • University of Chinese Academy of Sciences, Beijing, China
autor
  • Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
  • University of Chinese Academy of Sciences, Beijing, China
autor
  • School of Earth Science, Science and Technology Innovation Team on Fault Deformation, Sealing and Fluid Migration, Northeast Petroleum University, Daqing, China
autor
  • School of Earth Science, Science and Technology Innovation Team on Fault Deformation, Sealing and Fluid Migration, Northeast Petroleum University, Daqing, China
autor
  • Research Institute of Exploration and Development, Tarim Oilfield Company, PetroChina, Korla, China
Bibliografia
  • 1. Astafyeva E, Afraimovich EL (2004) Long-distance traveling ionospheric disturbances caused by the great Sumatra-Andaman earthquake on 26 December 2004, Institute of Solar-Terrestrial Physics SD RAS, P. O. Box 4026, Irkutsk, 664033, Russia
  • 2. Astafyeva E, Heki K, Kiryushkin V, Afraimovich E, Shalimov S (2009) Two-mode long-distance propagation of coseismic ionosphere disturbances. J Geophys Res 114(A10307):2009. https://doi.org/10.1029/2008JA013853
  • 3. Chakrabarty D, Bagiya M, Thanpi S, Iyer KN (2012) Solar EUV flux (0.1–50 nm), F10.7 cm flux, sunspot number and the total electron content in the crest region of equatorial ionization anomaly during the deep minimum between solar cycle 23 and 24. Indian J Radio Space Phys 41:110–120
  • 4. Choosakul N, Saito A, Iyemori T, Hashizume M (2009) Excitation of 4-min periodic ionospheric variations following the great Sumatra-Andaman earthquake in 2004. J Geophys Res 114:A10313. https://doi.org/10.1029/2008ja013915
  • 5. Cornely P-RJ (2003) Flexible prior models: three-dimensional ionospheric tomography. Radio Sci. 38:1087. https://doi.org/10.1029/2002rs002703
  • 6. Cornely P-R, Daniell R (2013) Anomalies in the Ionosphere around the Southern Faults of Haiti near the 2010 Earthquake. Natural Hazards NH13A-1589-2013, American Geophysical Union, December 2013
  • 7. Freund F (2010) Toward a unified solid state theory for pre-earthquake signals. Acta Geophys 58:719–766
  • 8. Freund F (2011) Pre-earthquake signals: underlying physical processes. J Asian Earth Sci 41:383–400
  • 9. Freund F, Takeuchi A, Lau BWS (2006) Electric currents streaming out of stressed igneous rocks: a step towards understanding pre-earthquake low frequency EM emissions. Phys Chem Earth 31:389–396
  • 10. Hammerstrom J, Cornely P-R (2016) Total electron content (TEC) variations and correlation with seismic activity over Japan. J Young Investig (JYI) 3. https://doi.org/10.22186/jyi.31.4.13-16
  • 11. Hasbi AM, Momani MA, Ali MAM, Misran N, Shiokawa K, Otsuka Y, Yumoto K (2009) Ionospheric and geomagnetic disturbances during the 2005 Sumatra earthquake. J Atmos Solar Terr Phys 71:1992–2005
  • 12. Hayakawa M, Fujinawa Y (1994) Electromagnetic phenomena related to earthquake predication. Terra Sci. Pub. Co., Tokyo
  • 13. Hayakawa M, Molchanov OA (eds) (2002) Seismo electromagnetics: lithosphere-atmosphere-ionosphere coupling. Terra Sci Pub. Co., Tokyo
  • 14. Heki K (2011) Ionospheric electron enhancement preceding the 2011 Tohoku-Oki earthquake. Geophys Res Lett. https://doi.org/10.1029/2011gl047908
  • 15. Heki K, Enomoto Y (2013) Preseismic ionospheric electron enhancements revisited. J Geophys Res Space Phys 118:6618–6626. https://doi.org/10.1002/jgra.50578
  • 16. Ho Y-Y, Liu J-Y, Parrot M, Pinçon J-L (2013) Temporal and spatial analyses on seismo-electric anomalies associated with the 27 February 2010 M = 8.8 Chile earthquake observed by DEMETER satellite. Natl Hazards Earth Syst Sci. 13:3281–3289. https://doi.org/10.5194/nhess-13-3281-2013. http://www.nat-hazards-earth-syst-sci.net/13/3281/2013
  • 17. Jin R, Jin S, Feng G (2012a) M_DCB: Matlab code for estimating GNSS satellite and receiver differential code biases. GPS Solut 16:541–548. https://doi.org/10.1007/s10291-012-0279-3 (Received: 19 April 2012/Accepted: 30 June 2012/Published online: 18 July 2012)
  • 18. Jin R, Jin S, Feng G (2012b) M_DCB: Matlab code for estimating GNSS satellite and receiver differential code biases. GPS Solut. 16:541–548. https://doi.org/10.1007/s10291-012-0279-3
  • 19. Jin S, Occhipinti G, Jin R (2015) GNSS ionospheric seismology: recent observation evidencesand characteristics. Earth Sci Rev 147(2015):54–64
  • 20. Kahinami Y, Kamogawa M, Tanioka Y, Watanabe S, Gusman AR, Liu J-Y, Watanabe Y, Mogi T (2012) Tsunamigenic ionospheric hole. Geophys. Res. Lett 39:L00G27. https://doi.org/10.1029/2011gl050159
  • 21. Kamogawa M, Kakinami Y (2013) Is an ionospheric electron enhancement preceeding the 2011 Tohoku-Oki earthquake a precursor. J Geophys Res Space Phys 118:1751–1754. https://doi.org/10.1002/jgra.50118
  • 22. Karato S-I (1999) Seismic anisotropy of the Earth’s inner core resulting from flow induced by Maxwell stresses. Nature 402:871–873. https://doi.org/10.1038/47235
  • 23. Kennett BLN, Engdahl ER (1991) Traveltimes for global earthquake location and phase identification. Geophys J Int 105:429–465
  • 24. Kennett BLN, Engdahl ER, Buland R (1995) Constraints on seismic velocities in the earth from traveltimes. Geophys J Int 122:108–124
  • 25. Liu JY, Tsai YB, Chen SW, Lee CP, Chen YC, Yen HY, Chang WY, Liu C (2006a) Giant ionospheric disturbances excited by the M9.3 Sumatra earthquake of 26 December 2004. Geophys Res Lett 33:L02103. https://doi.org/10.1029/2005GL023963
  • 26. Liu JY, Tsai YB, Ma KF, Chen YI, Tsai HF, Lin CH, Kamogawa M, Lee CP (2006b) Ionospheric GPS total electron content (TEC) disturbances triggered by the 26 December 2004 Indian Ocean tsunami. J Geophys Res 111:A05303. https://doi.org/10.1029/2005JA011200
  • 27. Liu JY, Tsai HF, Lin CH, Kamogawa M, Chen YI, Lin CH, Huang BS, Yu SB, Yeh YH (2010) Coseismic ionospheric disturbances triggered by the Chi‐Chi earthquake. J Geophys Res 115:A08303. https://doi.org/10.1029/2009JA014943
  • 28. Ma G, Maruyama T (2003) Derivation of TEC and estimation of instrumental biases from GEONET in Japan. Ann Geophys 21:2083–2093 (c European Geosciences Union 2003, April 2003)
  • 29. Mannucci AJ, Wilson BD, Yuan DN, Ho CH, Lindqwister UJ, Runge TF (1998) A global mapping technique for GPS-derived ionospheric total electron content measurements. Radio Sci 33(3):565–582
  • 30. Masci F, Thomas JN, Villani F, Secan JA, Rivera N (2014) On the onset of ionospheric precursors 40 min before strong earthquakes. J Geophys Res Space Phys 120:1383–1393. https://doi.org/10.1002/2014/ja020822
  • 31. Ng KK (2016) Prediction methods in solar sunspots cycles. Sci Rep 6:21028. https://doi.org/10.1038/srep21028
  • 32. O’Brien M, Cornely P-R (2015) Analyzing Anomalies in the Ionosphere Above Haiti Surrounding the 2010 Earthquake. J Young Investig
  • 33. Ohl AI (1966) Wolfs number prediction for the maximum of the cycle 20. Soln Dannye 12:84
  • 34. Otsuka Y, Ogawa T, Saito A, Tsugawa T, Fukao S, Miyazaki S (2002) A new technique for mapping of total electron content using GPS network in Japan. Earth Planets Space 54:63–70
  • 35. Oyama K-I, Kakinami Y, Liu JY, Abdu MA, Cheng CZ (2011) Latitudinal distribution of anomalous ion density as a precursor of a large earthquake. J Geophys Res 116:A04319. https://doi.org/10.1029/2010JA015948
  • 36. Parrot M, Tramutoli V, Liu TJY, Pulinets S, Ouzounov D, Genzano N, Lisi M, Hattori K, Namgaladze A (2016) Atmospheric and ionospheric coupling phenomena related to large earthquakes. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-2016-172(Published: 23 June 2016)
  • 37. Poupinet G, Pillet R, Souriau A (1983) Possible heterogeneity of the Earth’s core deduced from PKIKP travel times. Nature 305:204–206
  • 38. Pulinets SA, Liu JY (2004) Ionospheric variability unrelated to solar and geomagnetic activity. Adv Space Res 34:1926–1933
  • 39. Pulinets SA, Gaivoronskaya TV, Leiva Contreras A, Ciraolo L (2004) Correlation analysis technique revealing ionosphere precursors of earthquakes. Nat Hazards Earth Syst Sci 4:697–702
  • 40. Russell CT, Lugmann JG, Jian LK (2010) How an unprecedented a solar minimum? Rev Geophys 48:RG2004. https://doi.org/10.1029/2009rg000316
  • 41. Simon SJ, Zharkov SI, Zharkova VV (2014) Prediction of solar activity from solar background magnetic field variations in cycles 21–23. Astrophys J 795:46
  • 42. Song X, Helmberger DVA (1995) P wave velocity model of Earth’s core. J Geophys Res 100:9817–9830
  • 43. Song Q, Ding F, Yu T et al (2015) GPS detection of the coseismic ionospheric disturbances following the 12 May 2008 M7.9 Wenchuan earthquake in China. Sci China Earth Sci 58:151–158. https://doi.org/10.1007/s11430-014-5000-7
  • 44. Souriau A, Poupinet G (1991) The velocity profile at the base of the liquid core from PKP(BC + Cdiff) data: an argument in favor of radial inhomogeneity. Geophys Res Lett 18:2023–2026
  • 45. Souriau A, Roudil P (1995) Attenuation in the uppermost inner core from broad-band GEOSCOPE PKP data. Geophys J Int 123:572–587
  • 46. Yu W-C, Wen L, Niu F (2005) Seismic velocity structure in the earth’s outer core. J Geophys Res 110:B02302. https://doi.org/10.1029/2003jb002928
  • 47. Zou Z, Koper KD, Cormier VF (2008) The structure of the base of the outer core inferred from seismic waves diffracted around the inner core. J Geophys Res 113:B05314.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-791a6c13-d6a1-4d50-b490-89e0aad6b77a
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